Matching knowledge elements in concept maps using a similarity flooding algorithm

Byron Marshall, Hsinchun Chen, Therani Madhusudan

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Concept mapping systems used in education and knowledge management emphasize flexibility of representation to enhance learning and facilitate knowledge capture. Collections of concept maps exhibit terminology variance, informality, and organizational variation. These factors make it difficult to match elements between maps in comparison, retrieval, and merging processes. In this work, we add an element anchoring mechanism to a similarity flooding (SF) algorithm to match nodes and substructures between pairs of simulated maps and student-drawn concept maps. Experimental results show significant improvement over simple string matching with combined recall accuracy of 91% for conceptual nodes and concept → link → concept propositions in student-drawn maps.

Original languageEnglish (US)
Pages (from-to)1290-1306
Number of pages17
JournalDecision Support Systems
Volume42
Issue number3
DOIs
StatePublished - Dec 2006

Keywords

  • Computer assisted instruction
  • Concept mapping
  • Conceptual graphs
  • Semantic matching
  • Semantic networks

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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